
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')
altBaselinesfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'Energy_CellModuleTechCompare')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)


from platform import python_version 
print(python_version())

3.8.8


#https://www.learnui.design/tools/data-color-picker.html#palette
#color pallette - modify here for all graphs below
colorpalette=['#000000', #PV ICE baseline
              '#0079C1','#7030A0','#F6A01A', #extreme cases (3) long life, high eff, circular
                '#41B8FF','#EF5675','#FFC425', #hypothetical modules (3) 50 yr, recycleSi, perovskite
                '#467126','#5E9732','#9CD174','#DEF0D1'] #realistic cases (4) greens, perc, shj, topcon, irena

colormats = ['#00bfbf','#ff7f0e','#1f77be','#2ca02c','#d62728','#9467BD','#8C564B'] #colors for material plots       

import matplotlib as mpl #import matplotlib
from cycler import cycler #import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color=colorpalette) #reset the default color palette of mpl

plt.rcParams.update({'font.size': 14})
plt.rcParams['figure.figsize'] = (8, 6)

scennames_labels = ['PV_ICE','Ideal\nLong-Lived','Ideal\nHigh Eff','Ideal\nCircular',
                    'Hypothetical\n50-year PERC','Hypothetical\nRecycled Si','Hypothetical\nCircular Perovskite',
                    'PERC','SHJ','TOPCon','IRENA\nreg. loss'] 
#,'Lightweight'


#creating scenarios for identical power of multiple technologies, moved to below due to reordering
scennames_ex = ['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite'] #extreme boundaries
scennames_r = ['PV_ICE','r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA'] #realistic cases
scennames_hyp = ['PV_ICE', 'h_50PERC','h_RecycledPERC', 'h_Perovskite'] #hypothetical modules

MATERIALS = ['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')


#load in a baseline and materials for modification
import PV_ICE

sim1 = PV_ICE.Simulation(name='sim1', path=testfolder)
for scen in scennames_ex:
    sim1.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim1.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis


PV_ICE.__version__

'v0.2.0+418.gd2758f7.dirty'


sim1.scenario.keys()

dict_keys(['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite'])


celltech_modeff = pd.read_csv(os.path.join(supportMatfolder, 'output-celltech-modeffimprovements.csv'),index_col=0) #pull in module eff
celltech_aguse = pd.read_csv(os.path.join(supportMatfolder, 'output-celltech-Agusageimprovements.csv'),index_col=0) #pull in Ag use


celltech_modeff.loc[2020]

PERC      22.8
SHJ       23.5
TOPCon    23.2
Name: 2020, dtype: float64


#glass-glass package mass per area calculation
#ITRPV 2022 Figs 36 and 38, we are assuming that the front and back glass heave equal thickness of 2.5mm
density_glass = 2500*1000 # g/m^3 
glassperm2 = (2.5/1000)* 2 * density_glass
print('The mass per module area of glass is '+str(glassperm2)+' g/m^2 for all modules with a glass-glass package')

The mass per module area of glass is 12500.0 g/m^2 for all modules with a glass-glass package


timeshift = 2022-1995


#module efficiency modify for PERC
sim1.modifyScenario('ex_PERC_50', 'mod_eff', 24.5, start_year=2022) #PERC 2030 module eff


#silver modify for PERC
sim1.scenario['ex_PERC_50'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'PERC'], start_year=2022)
#old way
#sim1.scenario['PERC_50'].material['silver'].matdataIn_m.loc[timeshift:,'mat_massperm2'] = celltech_aguse.loc[2022:,'PERC'].values


#modify package to glass glass
sim1.scenario['ex_PERC_50'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022) #


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('ex_PERC_50', 'mod_degradation', 0.445, start_year=2022) #annual power degradation to reach 80% at 50 yrs
#T50
sim1.modifyScenario('ex_PERC_50', 'mod_reliability_t50', 56.07, start_year=2022)
#t90
sim1.modifyScenario('ex_PERC_50', 'mod_reliability_t90', 59.15, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('ex_PERC_50', 'mod_lifetime', 50, start_year=2022) #project lifetime of 50 years


#Merchant Tail set high
sim1.modifyScenario('ex_PERC_50', 'mod_MerchantTail', 100, start_year=2022) #all installations stay for merchant tail
#Change recycling?


#idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
#df_higheff = pd.DataFrame(index=idx_temp, columns=['mod_eff_p'], dtype=float)
#df_higheff.loc[2022] = 22.0
#df_higheff.loc[2030] = 25.0
#df_higheff.loc[2050] = 30.0
#df_higheff.interpolate(inplace=True)
#module efficiency modify for PERC
sim1.modifyScenario('ex_High_eff', 'mod_eff', 30.0, start_year=2022) #changing module eff


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('ex_High_eff', 'mod_degradation', 0.7, start_year=2022) #standard current degrdation
#T50
sim1.modifyScenario('ex_High_eff', 'mod_reliability_t50', 29, start_year=2022)
#t90
sim1.modifyScenario('ex_High_eff', 'mod_reliability_t90', 32, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('ex_High_eff', 'mod_lifetime', 25, start_year=2022) #project lifetime of 25 years


#2022 module eff = 17.9% #https://www.nrel.gov/pv/assets/pdfs/champion-module-efficiencies-rev220401b.pdf
#2050 module eff = 27.3% # Sofia et al 2019 4T future
idx_perovskite_eff = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_perovskite_eff = pd.DataFrame(index=idx_perovskite_eff, columns=['mod_eff_p'], dtype=float)
df_perovskite_eff.loc[2022] = 17.9
df_perovskite_eff.loc[2030] = 27.3
df_perovskite_eff.loc[2050] = 27.3
df_perovskite_eff.interpolate(inplace=True)


#module efficiency modify for PERC
sim1.modifyScenario('ex_Perovskite', 'mod_eff', 27.3, start_year=2022) #changing module eff


#modify package to glass glass
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('ex_Perovskite', 'mod_degradation', 1.47, start_year=2022) #annual power degradation
#Mod Project Lifetime
sim1.modifyScenario('ex_Perovskite', 'mod_lifetime', 15, start_year=2022) #project lifetime of 15 years
#T50
sim1.modifyScenario('ex_Perovskite', 'mod_reliability_t50', 19, start_year=2022)
#t90
sim1.modifyScenario('ex_Perovskite', 'mod_reliability_t90', 22, start_year=2022)


#As Circular as possible
#100% collection rate
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_collection_eff', 100.0, start_year=2022) #100% collection
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg1_landfill', 0.0, start_year=2022) #100% collection
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb1_landfill', 0.0, start_year=2022) #100% collection

# remanufacturing
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg3_reMFG', 100.0, start_year=2022) #all modules attempt remfg
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_sp_reMFG_recycle', 100.0, start_year=2022) # recycle if can't remfg
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb3_reMFG', 100.0, start_year=2022) # remfg bad mods too
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_reMFG_yield', 98.0, start_year=2022) # REMFG YIELD 98%

#set all other paths to 0
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg0_resell', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg1_landfill', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg2_stored', 0.0, start_year=2022) #
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pg4_recycled', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb1_landfill', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb2_stored', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_EOL_pb4_recycled', 0.0, start_year=2022) # 
sim1.modifyScenario('ex_Perovskite', 'mod_Repair', 0.0, start_year=2022) #
sim1.modifyScenario('ex_Perovskite', 'mod_MerchantTail', 0.0, start_year=2022) #


#Material Remanufacture
#Glass
#mfg scrap
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #99% yield
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 100.0, start_year=2022) #send all to remfg
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send none to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_ReMFG_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('glass', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild


#silicon ReMFG 
#mfg scrap
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 100.0, start_year=2022) #send to remfg
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_ReMFG_yield', 98.0, start_year=2022) #98% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 0.0, start_year=2022) #send to remfg
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#aluminium_frames recycle
#mfg scrap
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
sim1.scenario['ex_Perovskite'].modifyMaterials('aluminium_frames', 'mat_ReMFG_yield', 0.0, start_year=2022) #0% yeild


#module efficiency modify for PERC
#sim1.modifyScenario('ex_Lightweight', 'mod_eff', celltech_modeff.loc[2022:,'PERC'], start_year=2022) #changing module eff


#2mm glass calculation
#lightweightglass = (2.0/1000)* 1 * density_glass

#50% encapsulant
#light_encap = pd.Series(sim1.scenario['PV_ICE'].material['encapsulant'].matdataIn_m.loc[timeshift:,'mat_massperm2']*0.5)

#10% silicon
#light_Si = pd.Series(sim1.scenario['PV_ICE'].material['silicon'].matdataIn_m.loc[timeshift:,'mat_massperm2']*0.1)

#modify BOM
#sim1.scenario['ex_Lightweight'].modifyMaterials('glass', 'mat_massperm2', lightweightglass, start_year=2022) #glass
#sim1.scenario['ex_Lightweight'].modifyMaterials('aluminium_frames', 'mat_massperm2', 0.0, start_year=2022) #frameless
#sim1.scenario['ex_Lightweight'].modifyMaterials('encapsulant', 'mat_massperm2', light_encap, start_year=2022) #50% encapsulant
#sim1.scenario['ex_Lightweight'].modifyMaterials('silicon', 'mat_massperm2', light_Si, start_year=2022) #10% silicon


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
#sim1.modifyScenario('ex_Lightweight', 'mod_degradation', 0.7, start_year=2022) #annual power degradation
#Mod Project Lifetime
#sim1.modifyScenario('ex_Lightweight', 'mod_lifetime', 25, start_year=2022) #project lifetime of 15 years
#T50
#sim1.modifyScenario('ex_Lightweight', 'mod_reliability_t50', 29, start_year=2022) #this will get changed with IRENA alpha/b
#t90
#sim1.modifyScenario('ex_Lightweight', 'mod_reliability_t90', 32, start_year=2022) #this will get changed with IRENA alpha/b


#len(sim1.scenario['ex_Lightweight'].material['silicon'].matdataIn_m)


#modified module
moduleinput_m_r_50PERC = os.path.join(altBaselinesfolder, 'mod_r_50PERC.csv')
sim1.createScenario(name='h_50PERC', massmodulefile=moduleinput_m_r_50PERC, energymodulefile=moduleFile_e) 

#load standard materials
for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['h_50PERC'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#glass recycling
#create improvement series
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_glass_target = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_glass_target.loc[2022] = 0
df_glass_target.loc[2030] = 100
df_glass_target.loc[2050] = 100
df_glass_target.interpolate(inplace=True)

df_glass_lowrecycle = pd.DataFrame(index=idx_temp, columns=['mat_EOL_Recycled_into_HQ'], dtype=float)
df_glass_lowrecycle.loc[2022] = 0
df_glass_lowrecycle.loc[2030] = 0
df_glass_lowrecycle.loc[2050] = 50
df_glass_lowrecycle.interpolate(inplace=True)

#eol
sim1.scenario['h_50PERC'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_50PERC'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 
                                          df_glass_target.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) #send to recycle

sim1.scenario['h_50PERC'].modifyMaterials('glass', 'mat_EOL_Recycled_into_HQ', 
                                          df_glass_lowrecycle.loc[2022:,'mat_EOL_Recycled_into_HQ'], start_year=2022) #all HQ
sim1.scenario['h_50PERC'].modifyMaterials('glass', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#silicon recycled
#create improvement series
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_si_target = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_si_target.loc[2022] = 0
df_si_target.loc[2030] = 0
df_si_target.loc[2050] = 50
df_si_target.interpolate(inplace=True)

#eol
sim1.scenario['h_50PERC'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_50PERC'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 
                                          df_si_target.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) #send to recycle

sim1.scenario['h_50PERC'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_50PERC'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#aluminium_frames recycle
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_al_target = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_al_target.loc[2022] = 0
df_al_target.loc[2030] = 100
df_al_target.loc[2050] = 100
df_al_target.interpolate(inplace=True)

df_al_lowrecycle = pd.DataFrame(index=idx_temp, columns=['mat_EOL_Recycled_into_HQ'], dtype=float)
df_al_lowrecycle.loc[2022] = 0
df_al_lowrecycle.loc[2030] = 100
df_al_lowrecycle.loc[2050] = 100
df_al_lowrecycle.interpolate(inplace=True)

df_al_lowCL = pd.DataFrame(index=idx_temp, columns=['mat_EOL_RecycledHQ_Reused4MFG'], dtype=float)
df_al_lowCL.loc[2022] = 0
df_al_lowCL.loc[2030] = 20
df_al_lowCL.loc[2050] = 50
df_al_lowCL.interpolate(inplace=True)

#eol
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 
                                          df_al_target.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) #send to recycle
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 
                                          df_al_lowrecycle.loc[2022:,'mat_EOL_Recycled_into_HQ'], start_year=2022) #all HQ
sim1.scenario['h_50PERC'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 
                                          df_al_lowCL.loc[2022:,'mat_EOL_RecycledHQ_Reused4MFG'], start_year=2022) #closed-loop


#modified module
moduleinput_m_r_RecyclePERC = os.path.join(altBaselinesfolder, 'mod_r_PERCrecycleSi.csv')
sim1.createScenario(name='h_RecycledPERC', massmodulefile=moduleinput_m_r_RecyclePERC, energymodulefile=moduleFile_e) 

for mat in range (0, len(MATERIALS)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
    sim1.scenario['h_RecycledPERC'].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#glass recycling
#eol
sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
#sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_EOL_Recycled_into_HQ', 0.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('glass', 'mat_EOL_RecycledHQ_Reused4MFG', 0.0, start_year=2022) #closed-loop

#silicon recycled
#mfg scrap
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop

#aluminium_frames recycle
#mfg scrap
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop

#silver recycling improvement
#create improvement series
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_silver_recycleimprove = pd.DataFrame(index=idx_temp, columns=['mat_PG4_Recycling_target'], dtype=float)
df_silver_recycleimprove.loc[2022] = 0
df_silver_recycleimprove.loc[2030] = 40
df_silver_recycleimprove.loc[2050] = 100
df_silver_recycleimprove.interpolate(inplace=True)

#eol
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_PG4_Recycling_target',
                                        df_silver_recycleimprove.loc[2022:,'mat_PG4_Recycling_target'], start_year=2022) 
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_RecycledPERC'].modifyMaterials('silver', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#the cool feature of this module was that the wafer was directly put into the Cz process
#therefore, we need to modify the recycling energy to reflect this
#this energy profile is output by the (baseline development) Energy Silicon Manufacturing journal.
altHQRecycle_e = pd.read_csv(os.path.join(supportMatfolder, 'output_energy_silicon_eol_recycleHQ_ALT.csv'), index_col=0)
altHQRecycle_e


#modify silicon recycling energy
sim1.scenario['h_RecycledPERC'].modifyMaterialEnergy('silicon', 'e_mat_Recycled_HQ', altHQRecycle_e.loc[2022:,'E_MFG_kWhpkg'], start_year=2022)
sim1.scenario['h_RecycledPERC'].modifyMaterialEnergy('silicon', 'e_mat_Recycled_HQ_fuelfraction', altHQRecycle_e.loc[2022:,'E_mfgFuelFraction'], start_year=2022)


sim1.scenario['h_RecycledPERC'].material['silicon'].matdataIn_e.iloc[25:30,]


#modified module
moduleinput_m_r_perovskite = os.path.join(altBaselinesfolder, 'mod_r_perovskite.csv')
sim1.createScenario(name='h_Perovskite', massmodulefile=moduleinput_m_r_perovskite, energymodulefile=moduleFile_e) 

#modified materials
#glass
matinput_glass_m_remfg = os.path.join(altBaselinesfolder,'material_mass_glass_r_perovskite.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['h_Perovskite'].addMaterial('glass', massmatfile=matinput_glass_m_remfg, energymatfile=matbaseline_e_glass)

#silicon
matinput_silicon_m_remfg = os.path.join(altBaselinesfolder,'material_mass_silicon_r_perovskite.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['h_Perovskite'].addMaterial('silicon', massmatfile=matinput_silicon_m_remfg, energymatfile=matbaseline_e_si)

#load standard materials
subMats =['silver', 'aluminium_frames','copper','encapsulant', 'backsheet']
for mat in range (0, len(subMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+subMats[mat]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+subMats[mat]+'.csv')
    sim1.scenario['h_Perovskite'].addMaterial(subMats[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#aluminium_frames recycle
#eol
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['h_Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG',100.0, start_year=2022) #closed-loop


altBaselinesfolder

'C:\\Users\\hmirletz\\Documents\\GitHub\\PV_ICE\\PV_ICE\\baselines\\Energy_CellModuleTechCompare'


#modified module
moduleinput_m_r_PERC = os.path.join(altBaselinesfolder, 'mod_r_PERC.csv')
sim1.createScenario(name='r_PERC', massmodulefile=moduleinput_m_r_PERC, energymodulefile=moduleFile_e)


#module efficiency modify for PERC
sim1.modifyScenario('r_PERC', 'mod_eff', celltech_modeff.loc[2022:,'PERC'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_PERC'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_PERC'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_PERC'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_PERC'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_PERC'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modify silver usage for PERC
sim1.scenario['r_PERC'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'PERC'], start_year=2022)


#modify package to glass glass
sim1.scenario['r_PERC'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#modified module
moduleinput_m_r_SHJ = os.path.join(altBaselinesfolder, 'mod_r_SHJ.csv')
sim1.createScenario(name='r_SHJ', massmodulefile=moduleinput_m_r_SHJ, energymodulefile=moduleFile_e)


#module efficiency modify for SHJ
sim1.modifyScenario('r_SHJ', 'mod_eff', celltech_modeff.loc[2022:,'SHJ'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#load modified materials
#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_SHJ'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_SHJ'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_SHJ'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_SHJ'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_SHJ'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modify silver usage for SHJ
sim1.scenario['r_SHJ'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'SHJ'], start_year=2022)


#modify package to glass glass
sim1.scenario['r_SHJ'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


plt.plot(sim1.scenario['r_SHJ'].material['silicon'].matdataIn_m.iloc[:,0],sim1.scenario['r_SHJ'].material['silicon'].matdataIn_m.iloc[:,3:])
plt.legend(sim1.scenario['PV_ICE'].material['silicon'].matdataIn_m.iloc[:,3:].columns, 
           bbox_to_anchor=(0, -0.1), loc='upper left', ncol=1)
plt.title('Silicon Management Evolution')

Text(0.5, 1.0, 'Silicon Management Evolution')


plt.plot(sim1.scenario['r_SHJ'].material['glass'].matdataIn_m.iloc[:,0],sim1.scenario['r_SHJ'].material['glass'].matdataIn_m.iloc[:,3:])
plt.legend(sim1.scenario['PV_ICE'].material['glass'].matdataIn_m.iloc[:,3:].columns, 
           bbox_to_anchor=(0, -0.1), loc='upper left', ncol=1)
plt.title('Glass Management Evolution')

Text(0.5, 1.0, 'Glass Management Evolution')


plt.plot(sim1.scenario['r_SHJ'].material['aluminium_frames'].matdataIn_m.iloc[:,0],sim1.scenario['r_SHJ'].material['aluminium_frames'].matdataIn_m.iloc[:,3:])
plt.legend(sim1.scenario['PV_ICE'].material['aluminium_frames'].matdataIn_m.iloc[:,3:].columns, 
           bbox_to_anchor=(0, -0.1), loc='upper left', ncol=1)
plt.title('Glass Management Evolution')

Text(0.5, 1.0, 'Glass Management Evolution')


plt.plot(sim1.scenario['r_SHJ'].material['silver'].matdataIn_m.iloc[:,0],sim1.scenario['r_SHJ'].material['silver'].matdataIn_m.iloc[:,3:])
plt.legend(sim1.scenario['PV_ICE'].material['aluminium_frames'].matdataIn_m.iloc[:,3:].columns, 
           bbox_to_anchor=(0, -0.1), loc='upper left', ncol=1)
plt.title('Glass Management Evolution')

Text(0.5, 1.0, 'Glass Management Evolution')


#modified module
moduleinput_m_r_TOPCon = os.path.join(altBaselinesfolder, 'mod_r_TOPCon.csv')
sim1.createScenario(name='r_TOPCon', massmodulefile=moduleinput_m_r_TOPCon, energymodulefile=moduleFile_e)


#module efficiency modify
sim1.modifyScenario('r_TOPCon', 'mod_eff', celltech_modeff.loc[2022:,'TOPCon'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_TOPCon'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_TOPCon'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_TOPCon'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_TOPCon'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_TOPCon'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#modify silver usage for PERC
sim1.scenario['r_TOPCon'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'TOPCon'], start_year=2022)


#modify package to glass glass
sim1.scenario['r_TOPCon'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#modified module
moduleinput_m_r_IRENA = os.path.join(altBaselinesfolder, 'mod_r_IRENAregloss.csv')
sim1.createScenario(name='r_IRENA', massmodulefile=moduleinput_m_r_IRENA, energymodulefile=moduleFile_e)


#modified materials
#glass
matinput_glass_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_glass_improvedRecycle.csv')
matbaseline_e_glass = os.path.join(baselinesfolder,'baseline_material_energy_glass.csv')
sim1.scenario['r_IRENA'].addMaterial('glass', massmatfile=matinput_glass_m_improvedRecycle, energymatfile=matbaseline_e_glass)
#silicon
matinput_silicon_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silicon_improvedRecycle.csv')
matbaseline_e_si = os.path.join(baselinesfolder,'baseline_material_energy_silicon.csv')
sim1.scenario['r_IRENA'].addMaterial('silicon', massmatfile=matinput_silicon_m_improvedRecycle, energymatfile=matbaseline_e_si)
#silver
matinput_silver_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_silver_improvedRecycle.csv')
matbaseline_e_ag = os.path.join(baselinesfolder,'baseline_material_energy_silver.csv')
sim1.scenario['r_IRENA'].addMaterial('silver', massmatfile=matinput_silver_m_improvedRecycle, energymatfile=matbaseline_e_ag)
#aluminium frames
matinput_Al_m_improvedRecycle = os.path.join(altBaselinesfolder,'material_mass_aluminium_frames_improvedRecycle.csv')
matbaseline_e_al = os.path.join(baselinesfolder,'baseline_material_energy_aluminium_frames.csv')
sim1.scenario['r_IRENA'].addMaterial('aluminium_frames', massmatfile=matinput_Al_m_improvedRecycle, energymatfile=matbaseline_e_al)

#standard materials
baselineMats=['copper', 'encapsulant', 'backsheet']
for mats in range (0, len(baselineMats)):
    matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+baselineMats[mats]+'.csv')
    matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+baselineMats[mats]+'.csv')
    sim1.scenario['r_IRENA'].addMaterial(baselineMats[mats], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


sim1.modifyScenario('r_IRENA', 'mod_degradation', 0.9, start_year=2022)


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim1.trim_Years(startYear=2000, endYear=2100)

Trimming and extending  PV_ICE
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Energy, years now encompass  2000  to  2100
PV_ICE : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_PERC_50
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
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ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Energy, years now encompass  2000  to  2100
ex_PERC_50 : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_High_eff
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Energy, years now encompass  2000  to  2100
ex_High_eff : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  ex_Perovskite
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
ex_Perovskite : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  h_50PERC
h_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_50PERC : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  h_RecycledPERC
h_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC : Data trimmed for Energy, years now encompass  2000  to  2100
h_RecycledPERC : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  h_Perovskite
h_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite : Data trimmed for Energy, years now encompass  2000  to  2100
h_Perovskite : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_PERC
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Energy, years now encompass  2000  to  2100
r_PERC : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_SHJ
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Energy, years now encompass  2000  to  2100
r_SHJ : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_TOPCon
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Energy, years now encompass  2000  to  2100
r_TOPCon : Data trimmed for Mass, years now encompass  2000  to  2100
Trimming and extending  r_IRENA
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Energy, years now encompass  2000  to  2100
r_IRENA : Data trimmed for Mass, years now encompass  2000  to  2100


#check
#sim1.scenario['h_Perovskite'].dataIn_m


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)

fig, ax1 = plt.subplots()

ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
ax1.set_ylim(0,90)
ax1.set_xlim(2000,2100)
ax2 = ax1.twinx()
ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
ax2.set_ylabel('Annual Installations [TW]')
ax2.set_ylim(0,5)
plt.show()


#deployment projection for all scenarios
sim1.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', 
                    value= global_projection['World_annual_[MWdc]'], start_year=2000)
#for scen in scennames:
#    sim1.scenario[scen].dataIn_m.loc[0:len(global_projection.index-1),'new_Installed_Capacity_[MW]'] = global_projection['World_annual_[MWdc]'].values


sim1.scenario.keys()

dict_keys(['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'h_50PERC', 'h_RecycledPERC', 'h_Perovskite', 'r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA'])


scennames_ex = ['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite'] #extreme boundaries
scennames_r = ['PV_ICE','r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA'] #realistic cases
scennames_hyp = ['PV_ICE', 'h_50PERC','h_RecycledPERC', 'h_Perovskite'] #hypothetical modules

scennames_all = ['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite',
                 'h_50PERC','h_RecycledPERC', 'h_Perovskite',
                'r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA']


for scen in sim1.scenario.keys():
    print(sim1.scenario[scen].material.keys())

dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])
dict_keys(['glass', 'silicon', 'silver', 'aluminium_frames', 'copper', 'encapsulant', 'backsheet'])


IRENAregloss = {'alpha':5.692,
                   'beta':29.697}

idx_temp = pd.RangeIndex(start=2022,stop=2101,step=1) #create the index
evolve_weibull = pd.DataFrame(index=idx_temp, columns=['alpha','beta'], dtype=float)
evolve_weibull.loc[2022] = [5.3759,30]
evolve_weibull.loc[2030] = [5.1   ,35]
evolve_weibull.loc[2050] = [4.602 ,40.767]

evolve_weibull.interpolate(inplace=True)
evolve_weibull = round(evolve_weibull,3)
#evolve_weibull_temp = evolve_weibull.reset_index(drop=True)
#evolve_weibull_temp.to_dict(orient='dict')
#dict([(i,[a,b,c ]) for i, a,b,c in zip(df.ID, df.A,df.B,df.C)])
dict_weibull = {'alpha':evolve_weibull['alpha'].tolist(),
                'beta':evolve_weibull['beta'].tolist()}
evolve_weibull.to_csv(os.path.join(altBaselinesfolder,'evolve_weibull.csv'))


bifiFactors = {'PV_ICE':0.0,
               'ex_PERC_50':0.7, # ITRPV 2022, Fig. 58
               'ex_High_eff': 0.9,
               'ex_Perovskite': 0.0,
               'h_50PERC':0.7,
               'h_RecycledPERC': 0.6,
               'h_Perovskite': 0.0,
               'r_PERC':0.7,
               'r_SHJ':0.9, # ITRPV 2022, Fig. 58
               'r_TOPCon':0.8, # ITRPV 2022, Fig. 58              
                'r_IRENA':0.0 } 

#MAY NEED TO CHANGE TO BE DYNAMIC
#               'ex_Lightweight':0.0,


#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
idx_temp = pd.RangeIndex(start=2000,stop=2101,step=1) #create the index
df_temp = pd.DataFrame(index=idx_temp, columns=['bifi'], dtype=float)
bifiPathDict={}

for f in bifiFactors.keys(): #loop over module types
    bifi = df_temp.copy() #copy of df
    bifi['bifi'] = bifiFactors[f] #assign column
    bifipath = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi.to_csv(path_or_buf=bifipath, index_label='year') #create file
    bifiPathDict[str(f)] = bifipath
    #append bifi path to dict? or list?


for scen in sim1.scenario.keys(): #loop over scenarios
    if scen in ['r_IRENA']: #,'ex_Lightweight'
        sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen], weibullInputParams=IRENAregloss)
    else:
        sim1.calculateMassFlow(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_PERC_50
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_High_eff
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_Perovskite
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  2532456700.9151797  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  11518546.694268066  tonnes.
==> Working on Material :  silicon
ReMFG surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  71994785.19846186  tonnes.
Recycled surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  267559.9561905653  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  ex_Perovskite  =  252931173.78063294  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  h_50PERC
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  h_50PERC  =  10218339.404914325  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  h_50PERC  =  3022394.237767588  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  h_50PERC  =  27706932.65677402  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  h_RecycledPERC
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  h_RecycledPERC  =  97711618.73318519  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  h_RecycledPERC  =  574677.8975037739  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  h_RecycledPERC  =  334496785.83409405  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  h_Perovskite
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  h_Perovskite  =  2390139947.8364835  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  h_Perovskite  =  5602589.406206189  tonnes.
==> Working on Material :  silicon
ReMFG surplus End of Sim for Mat  silicon  Scenario  h_Perovskite  =  56308260.0495583  tonnes.
Recycled surplus End of Sim for Mat  silicon  Scenario  h_Perovskite  =  2134093.931282162  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  h_Perovskite  =  285947528.2536086  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  r_PERC
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_PERC  =  1376994924.2570825  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_PERC  =  41432216.863991305  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_PERC  =  263380.9404062508  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_PERC  =  213221599.14535347  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  r_SHJ
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_SHJ  =  1312550819.218989  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_SHJ  =  39557723.941515  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_SHJ  =  422341.10021999583  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_SHJ  =  203453885.5583714  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  r_TOPCon
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_TOPCon  =  1599376521.6742687  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_TOPCon  =  53220676.44524092  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_TOPCon  =  537615.7500202699  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_TOPCon  =  266122819.51848605  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet
>>>> Calculating Material Flows <<<<

Working on Scenario:  r_IRENA
********************
Finished Area+Power Generation Calculations
Warning: Paths 0 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P0+P2+P3+P4).
Warning: Paths B 1 through 4 add to above 100%;Fixing by Updating Landfill value to the remainder of100-(P2+P3+P4).
==> Working on Material :  glass
Recycled surplus End of Sim for Mat  glass  Scenario  r_IRENA  =  42584101349.58458  tonnes.
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  r_IRENA  =  1537469091.993155  tonnes.
==> Working on Material :  silver
Recycled surplus End of Sim for Mat  silver  Scenario  r_IRENA  =  8893380.068700368  tonnes.
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  r_IRENA  =  7055460638.342605  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


#sim1.calculateMassFlow()


#sim1.calculateMassFlow(scenarios=['ex_Lightweight','r_IRENA'], weibullInputParams=IRENAregloss)
#need to create an upgrade to weibull params to account for evolving params
#dict_weibull # evolve_weibull
#sim1.calculateMassFlow(scenarios=['r_IRENA'])


ii_yearly, ii_cumu = sim1.aggregateResults() #have to do this to get auto plots


#sim1.scenario['r_IRENA'].dataOut_m['WeibullParams']


effective_capacity = ii_yearly.filter(like='ActiveCapacity')
plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
plt.plot(effective_capacity/1e6, label=sim1.scenario.keys())
plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 90.26425956655)


effective_capacity = ii_yearly.filter(like='ActiveCapacity').filter(like='r_')
plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
plt.plot(effective_capacity/1e6, label=effective_capacity.columns)
plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 90.26425956655)


plt.plot(ii_yearly.filter(like='Decommisioned'))

[<matplotlib.lines.Line2D at 0x17f228ae280>,
 <matplotlib.lines.Line2D at 0x17f228ae2e0>,
 <matplotlib.lines.Line2D at 0x17f228ae400>,
 <matplotlib.lines.Line2D at 0x17f228ae520>,
 <matplotlib.lines.Line2D at 0x17f228ae640>,
 <matplotlib.lines.Line2D at 0x17f228a71c0>,
 <matplotlib.lines.Line2D at 0x17f228a7040>,
 <matplotlib.lines.Line2D at 0x17f228ae880>,
 <matplotlib.lines.Line2D at 0x17f228ae9a0>,
 <matplotlib.lines.Line2D at 0x17f228aeac0>,
 <matplotlib.lines.Line2D at 0x17f228aebe0>]


effective_capacity = ii_yearly.filter(like='ActiveCapacity').filter(like='ex_')
plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
plt.plot(effective_capacity/1e6, label=effective_capacity.columns)
plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 90.26425956655)


plt.plot(ii_yearly.filter(like='WasteAll_Module'))

[<matplotlib.lines.Line2D at 0x17f22c7cca0>,
 <matplotlib.lines.Line2D at 0x17f22c7cd00>,
 <matplotlib.lines.Line2D at 0x17f22c7ce20>,
 <matplotlib.lines.Line2D at 0x17f22c7cf40>,
 <matplotlib.lines.Line2D at 0x17f22c870a0>,
 <matplotlib.lines.Line2D at 0x17f22c6fbe0>,
 <matplotlib.lines.Line2D at 0x17f22c6fa60>,
 <matplotlib.lines.Line2D at 0x17f22c872e0>,
 <matplotlib.lines.Line2D at 0x17f22c87400>,
 <matplotlib.lines.Line2D at 0x17f22c87520>,
 <matplotlib.lines.Line2D at 0x17f22c87640>]


plt.plot(sim1.scenario['r_IRENA'].dataOut_m['Yearly_Sum_Power_disposedby_Failure'])
plt.plot(sim1.scenario['r_IRENA'].dataOut_m['Yearly_Sum_Power_disposedby_ProjectLifetime'])
#plt.plot(sim1.scenario['r_PERC'].dataOut_m['Yearly_Sum_Power_disposedby_Failure'])


sim1.scenario['r_IRENA'].dataOut_m.filter(like='disposed').columns.values

array(['Yearly_Sum_Area_disposed', 'Yearly_Sum_Area_disposedby_Failure',
       'Yearly_Sum_Area_disposedby_ProjectLifetime',
       'Yearly_Sum_Power_disposed', 'Yearly_Sum_Power_disposedby_Failure',
       'Yearly_Sum_Power_disposedby_ProjectLifetime'], dtype=object)


diagnose = ii_yearly.filter(like='r_IRENA').filter(like='WasteEOL')
diagnose.columns.values

array(['WasteEOL_glass_sim1_r_IRENA_[Tonnes]',
       'WasteEOL_silicon_sim1_r_IRENA_[Tonnes]',
       'WasteEOL_silver_sim1_r_IRENA_[Tonnes]',
       'WasteEOL_aluminium_frames_sim1_r_IRENA_[Tonnes]',
       'WasteEOL_copper_sim1_r_IRENA_[Tonnes]',
       'WasteEOL_encapsulant_sim1_r_IRENA_[Tonnes]',
       'WasteEOL_backsheet_sim1_r_IRENA_[Tonnes]',
       'WasteEOL_Module_sim1_r_IRENA_[Tonnes]'], dtype=object)


plt.plot(diagnose)

[<matplotlib.lines.Line2D at 0x17f23037ca0>,
 <matplotlib.lines.Line2D at 0x17f23037d00>,
 <matplotlib.lines.Line2D at 0x17f22d1bb80>,
 <matplotlib.lines.Line2D at 0x17f22d1bbb0>,
 <matplotlib.lines.Line2D at 0x17f23037f40>,
 <matplotlib.lines.Line2D at 0x17f230410a0>,
 <matplotlib.lines.Line2D at 0x17f230411c0>,
 <matplotlib.lines.Line2D at 0x17f230412e0>]


#currently takes ~40 mins to run with 7 materials and 12 scenarios

for row in range (0,len(sim1.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in sim1.scenario.keys(): #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim1.scenario[scen].dataOut_m['Installed_Capacity_[W]'][row])/1e6)  # MWATTS
        sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        #calculate flows for that scenario with it's bifi factor and modified weibull
        if scen in ['r_IRENA']: #,'ex_Lightweight'
            sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen], weibullInputParams=IRENAregloss)
        else:
            sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3996\3888156638.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  PV_ICE
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
ex_PERC_50
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_PERC_50
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3996\3888156638.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  ex_PERC_50
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
ex_High_eff
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_High_eff
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3996\3888156638.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silicon
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  ex_High_eff
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
ex_Perovskite
>>>> Calculating Material Flows <<<<

Working on Scenario:  ex_Perovskite
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3996\3888156638.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  2532456700.9151797  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  ex_Perovskite  =  11518546.694268066  tonnes.
==> Working on Material :  silicon
ReMFG surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  71994785.19846186  tonnes.
Recycled surplus End of Sim for Mat  silicon  Scenario  ex_Perovskite  =  267559.9561905653  tonnes.
==> Working on Material :  silver
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  ex_Perovskite  =  252931173.78063294  tonnes.
==> Working on Material :  copper
==> Working on Material :  encapsulant
==> Working on Material :  backsheet


>>>> Calculating Energy Flows <<<<

Working on Scenario:  ex_Perovskite
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  aluminium_frames
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  encapsulant
==> Working on Energy for Material :  backsheet
h_50PERC
>>>> Calculating Material Flows <<<<

Working on Scenario:  h_50PERC
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_3996\3888156638.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed


Under_Installment

4476364.366529092


#test with one scenario
#scen='r_Perovskite'
#df_compensate = pd.DataFrame(columns=['underinstalls[W]'],dtype=float)

#for row in range (0,len(sim1.scenario['PV_ICE'].dataIn_m)): #loop over length of years
#    print(row)
#    Under_Installment = global_projection.iloc[row,0] - ((sim1.scenario[scen].dataOut_m['Installed_Capacity_[W]'][row])/1e6)  # MWATTS
#    if row==0:
#        df_compensate.loc[row,'underinstalls[W]'] = Under_Installment
#    else:
#        if Under_Installment < 0.7*df_compensate.iloc[row-1,0]:
#            Under_Installment = df_compensate.iloc[row-1,0]
#    df_compensate.loc[row,'underinstalls[W]'] = Under_Installment
#    sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
    #calculate flows for that scenario with it's bifi factor and modified weibull
#    sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])


#plt.plot(df_compensate)


#Under_Installment


#aggregate results - mass
cc_yearly, cc_cumu = sim1.aggregateResults() #have to do this to get auto plots
cc_yearly.to_csv(os.path.join(testfolder, 'cc_10scen_yearly.csv'))
cc_cumu.to_csv(os.path.join(testfolder, 'cc_10scen_cumu.csv'))


#aggregate results - energy
allenergy, energyGen, energy_demands = sim1.aggregateEnergyResults()
allenergy.to_csv(os.path.join(testfolder, 'cc_10scen_allenergy.csv'))
energyGen.to_csv(os.path.join(testfolder, 'cc_10scen_energyGen.csv'))
energy_demands.to_csv(os.path.join(testfolder, 'cc_10scen_energy_demands.csv'))

C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:2056: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)


#read in saved results files for speed
cc_yearly = pd.read_csv(os.path.join(testfolder, 'cc_10scen_yearly.csv'), index_col='year')
cc_cumu = pd.read_csv(os.path.join(testfolder, 'cc_10scen_cumu.csv'), index_col='year')
allenergy = pd.read_csv(os.path.join(testfolder, 'cc_10scen_allenergy.csv'), index_col='year')
energyGen = pd.read_csv(os.path.join(testfolder, 'cc_10scen_energyGen.csv'), index_col='year')
energy_demands = pd.read_csv(os.path.join(testfolder, 'cc_10scen_energy_demands.csv'), index_col='year')


plt.plot(sim1.scenario['r_PERC'].dataOut_m['Installed_Capacity_[W]'])

[<matplotlib.lines.Line2D at 0x17f2270cd90>]


#sim1.scenario['PV_ICE'].dataOut_m['irradiance_stc'].head(2)
sim1.scenario.keys()

dict_keys(['PV_ICE', 'ex_PERC_50', 'ex_High_eff', 'ex_Perovskite', 'h_50PERC', 'h_RecycledPERC', 'h_Perovskite', 'r_PERC', 'r_SHJ', 'r_TOPCon', 'r_IRENA'])


sim1.scenario['h_RecycledPERC'].dataOut_m['WeibullParams'].tail(4)

97     {'alpha': 16.199948952041623, 'beta': 39.89240...
98     {'alpha': 16.199948952041623, 'beta': 39.89240...
99     {'alpha': 16.199948952041623, 'beta': 39.89240...
100    {'alpha': 16.199948952041623, 'beta': 39.89240...
Name: WeibullParams, dtype: object


effective_capacity = cc_yearly.filter(like='ActiveCapacity')
plt.plot(effective_capacity/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: With Replacements')
plt.ylim(0,)

(0.0, 90.26425956655)


annual_EoL = cc_yearly.filter(like='DecommisionedCapacity')
plt.plot(annual_EoL/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Annual EoL [TW]')
plt.title('Annual Decommissions [TW]')
plt.ylim(0,)

(0.0, 329.8185753278499)


annual_installs = cc_yearly.filter(like='newInstalled')
plt.plot(annual_installs/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Annual installed [TW]')
plt.title('Annual Installs with Replacements')
plt.ylim(0,)

(0.0, 8.54311618686425)


cumu_installs_annually = cc_cumu.filter(like='newInstalled')
plt.plot(cumu_installs_annually/1e6)
plt.legend(sim1.scenario.keys())
plt.ylabel('Cumulative installed [TW]')
plt.title('Replacements Adjusted Deployment Curve \n Cumulative Installs with Replacements')
plt.ylim(0,)

(0.0, 420.0828348943998)


cumu_installs = cc_cumu.filter(like='newInstalled')
plt.bar(scennames_labels, cumu_installs.loc[2100]/1e6, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.xticks(rotation=90)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')
plt.ylim(0,410)

(0.0, 410.0)


cumulative_nameplate_installs = global_projection.loc[2100,'World_cum'] #MW
print('The nameplate installations for energy transition and through 2100 are '+str(cumulative_nameplate_installs/1e6)+' TW.')

The nameplate installations for energy transition and through 2100 are 85.966 TW.


global_projection['World_annual_[MWdc]'].sum()

85966000.0


Additional_installs = cumu_installs.loc[2100]-global_projection.loc[2100,'World_cum']
plt.bar(scennames_labels, Additional_installs/1e6, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.ylabel('Cumulative Replacements [TW]')
plt.title('Replacements Required by Technology')
plt.xticks(rotation=90)
plt.ylim(0,410)

(0.0, 410.0)


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
plt.bar(scennames_labels, cumu_virgin_module.loc[2100]/1e9, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, 'Ideal\nLong-Lived'),
  Text(2.0, 0, 'Ideal\nHigh Eff'),
  Text(3.0, 0, 'Ideal\nCircular'),
  Text(4.0, 0, 'Hypothetical\n50-year PERC'),
  Text(5.0, 0, 'Hypothetical\nRecycled Si'),
  Text(6.0, 0, 'Hypothetical\nCircular Perovskite'),
  Text(7.0, 0, 'PERC'),
  Text(8.0, 0, 'SHJ'),
  Text(9.0, 0, 'TOPCon'),
  Text(10.0, 0, 'IRENA\nreg. loss')])


recycledperc_virginstock = cc_cumu.filter(like='VirginStock').filter(like='Recycled')
recycledperc_virginstock.drop('VirginStock_Module_sim1_h_RecycledPERC_[Tonnes]',axis=1, inplace=True)
plt.bar(recycledperc_virginstock.columns, recycledperc_virginstock.loc[2100]/1e6, tick_label = MATERIALS, color=colormats)
plt.ylabel('Million Metric tonnes')

Text(0, 0.5, 'Million Metric tonnes')


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
plt.plot(cumu_virgin_module/1e9)
plt.legend(scennames_labels)
plt.title('Cumulative Virgin Material Extraction: Module')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
plt.ylim(0,)

(0.0, 10.952169961082657)


annual_virgin_module = cc_yearly.filter(like='VirginStock_Module')
plt.plot(annual_virgin_module/1e6)
plt.legend(scennames_labels)
plt.title('Annual Virgin Material Extraction: Module')
plt.ylabel('Virgin Material Requirements\n[million tonnes]')
plt.ylim(0,)

(0.0, 332.92224345638414)


#print out masses for stacked bar charts
#mass_cumu = cc_cumu.loc[[2100]].filter(like='PV_ICE_[Tonnes]')
mass_cumu_virgin = cc_cumu.filter(like='VirginStock').loc[2100]
mass_cumu_wasteall = cc_cumu.filter(like='WasteAll').loc[2100]
mass_cumu = pd.concat([mass_cumu_virgin,mass_cumu_wasteall])
mass_cumu.to_csv(os.path.join(testfolder, 'cc_cumu_mass_2100results.csv'))


cc_cumu.to_csv(os.path.join(testfolder, 'cc_cumu_mass_results.csv'))


cumu_lifecycle_wastes = cc_cumu.filter(like='WasteAll_Module')

plt.bar(scennames_labels, cumu_lifecycle_wastes.loc[2100]/1e9, 
        tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.title('Cumulative Lifecycle Wastes')
plt.ylabel('Lifecycle Wastes\n[billion tonnes]')
plt.xticks(rotation=90)

(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]),
 [Text(0.0, 0, 'PV_ICE'),
  Text(1.0, 0, 'Ideal\nLong-Lived'),
  Text(2.0, 0, 'Ideal\nHigh Eff'),
  Text(3.0, 0, 'Ideal\nCircular'),
  Text(4.0, 0, 'Hypothetical\n50-year PERC'),
  Text(5.0, 0, 'Hypothetical\nRecycled Si'),
  Text(6.0, 0, 'Hypothetical\nCircular Perovskite'),
  Text(7.0, 0, 'PERC'),
  Text(8.0, 0, 'SHJ'),
  Text(9.0, 0, 'TOPCon'),
  Text(10.0, 0, 'IRENA\nreg. loss')])


annual_waste_module = cc_yearly.filter(like='WasteAll_Module')
plt.plot(annual_waste_module/1e6)
plt.legend(scennames_labels)
plt.title('Annual Waste: Module')
plt.ylabel('Lifecycle Wastes\n[million tonnes]')
plt.ylim(0,)

(0.0, 2953.9777196793398)


sim1.scenario['r_IRENA'].dataOut_m.loc[45:,]


sim1.scenario['r_SHJ'].dataOut_e


#energyGen = allenergy.filter(like='e_out_annual')
plt.plot(energyGen/1e12)
plt.legend(scennames_labels)
plt.title('Annual Energy Generation')
plt.ylabel('Energy Generation [TWh]')
plt.ylim(0,)


e_annual_sumDemands = energy_demands.filter(like='demand_total')


plt.plot(e_annual_sumDemands/1e12)
plt.legend(scennames_labels)
plt.title('Annual Lifecycle Energy Demands')
plt.ylabel('Energy Demands\n[TWh]')
plt.ylim(0,)


e_annual_sumDemands_cumu = e_annual_sumDemands.cumsum()


plt.bar(e_annual_sumDemands_cumu.columns, e_annual_sumDemands_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Cumulative Lifecycle Energy Demands')
plt.ylabel('Cumulative Energy Demands\n[TWh]')
plt.xticks(rotation=90)


e_fuels = energy_demands.filter(like='_fuel')
e_energydemands = energy_demands.loc[:,~energy_demands.columns.isin(e_fuels.columns)]
e_mfging_bymat = pd.DataFrame()
for scen in sim1.scenario.keys():
    e_energy_mfg_scen = e_energydemands.filter(like=scen).filter(like='_MFG_')
    e_energy_mod_mfg_scen = energy_demands.filter(like=scen).filter(like='mod_MFG')
    e_mfging_bymat = pd.concat([e_mfging_bymat,e_energy_mfg_scen,e_energy_mod_mfg_scen], axis=1)


cum_e_mfging_bymat = e_mfging_bymat.cumsum().loc[2100]
cum_e_mfging_bymat


#run 2x rn??
#energy demands by material mfging
plt.bar(e_annual_sumDemands_cumu.columns, e_annual_sumDemands_cumu.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)

#Materials
cume_mfg_glass = cum_e_mfging_bymat.filter(like='glass')
cume_mfg_silicon = cum_e_mfging_bymat.filter(like='silicon')
cume_mfg_silver = cum_e_mfging_bymat.filter(like='silver')
cume_mfg_aluminium_frames = cum_e_mfging_bymat.filter(like='aluminium_frames')
cume_mfg_copper = cum_e_mfging_bymat.filter(like='copper')
cume_mfg_encapsulant = cum_e_mfging_bymat.filter(like='encapsulant')
cume_mfg_backsheet = cum_e_mfging_bymat.filter(like='backsheet')
cume_mfg_mod = cum_e_mfging_bymat.filter(like='mod')

bot_ag = cume_mfg_glass.values+cume_mfg_silicon.values
bot_al = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values
bot_cu = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values
bot_encap = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values+cume_mfg_copper.values
bot_back = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values+cume_mfg_copper.values+cume_mfg_encapsulant.values
bot_mod = cume_mfg_glass.values+cume_mfg_silicon.values+cume_mfg_silver.values+cume_mfg_aluminium_frames.values+cume_mfg_copper.values+cume_mfg_encapsulant.values+cume_mfg_backsheet.values

plt.bar(scennames_labels, cume_mfg_glass, color = colormats[0])
plt.bar(scennames_labels, cume_mfg_silicon, bottom = cume_mfg_glass, color=colormats[1])
plt.bar(scennames_labels, cume_mfg_silver, bottom = bot_ag, color=colormats[2])
plt.bar(scennames_labels, cume_mfg_aluminium_frames, bottom = bot_al, color=colormats[3])
plt.bar(scennames_labels, cume_mfg_copper, bottom = bot_cu , color=colormats[4])
plt.bar(scennames_labels, cume_mfg_encapsulant, bottom = bot_encap ,color=colormats[5])
plt.bar(scennames_labels, cume_mfg_backsheet, bottom = bot_back, color=colormats[6])
plt.bar(scennames_labels, cume_mfg_mod, bottom = bot_back, color='white')

plt.xticks(rotation=90)

plt.legend(MATERIALS)


energyGen_cumu = energyGen.cumsum()
energyGen_cumu.columns = e_annual_sumDemands_cumu.columns = scennames_labels
netEnergy_cumu = energyGen_cumu.loc[[2100]] - e_annual_sumDemands_cumu.loc[[2100]]


e_annual_sumDemands_cumu.loc[[2100]]


netEnergy_cumu


plt.bar(netEnergy_cumu.columns, netEnergy_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy [TWh]')
#plt.ylim(4e6,5.5e6)
plt.xticks(rotation=90)


netEnergy_relative = netEnergy_cumu - netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_relative
plt.bar(netEnergy_relative.columns, netEnergy_relative.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Cumulatively Net Energy Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy [TWh]')
plt.xticks(rotation=90)


netEnergy_cumu_norm = netEnergy_cumu/netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_cumu_norm_waterfall = netEnergy_cumu_norm-1
netEnergy_cumu_norm


plt.bar(netEnergy_cumu.columns, netEnergy_cumu_norm_waterfall.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy')
#plt.ylim(-0.026,0.005)
plt.plot(0.0, lw=2)
plt.xticks(rotation=90)


plt.bar(netEnergy_cumu.columns, netEnergy_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy\n[TWh]')
plt.xticks(rotation=90)


energyGen.columns = e_annual_sumDemands.columns = scennames_labels
annual_net_energy = energyGen - e_annual_sumDemands


plt.plot(annual_net_energy/1e12)
plt.legend(scennames_labels)
plt.title('Annual Net Energy')
plt.ylabel('Net Energy (Eout - Ein)\n[TWh]')
plt.ylim(0,)
plt.xlim(2000,2100)


eroi_sys_cumu = energyGen_cumu/e_annual_sumDemands_cumu


plt.bar(eroi_sys_cumu.columns, eroi_sys_cumu.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI of all Deployments')
plt.ylabel('Unitless')
plt.xticks(rotation=90)


#cumulatively
netEnergy_cumu.columns = cumu_virgin_module.columns = cumu_lifecycle_wastes.columns = scennames_labels

CEETmetric = (netEnergy_cumu.loc[2100]/1e6)/ (cumu_virgin_module.loc[2100]+ cumu_lifecycle_wastes.loc[2100])


plt.bar(CEETmetric.index, CEETmetric, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('CETE Metric')
plt.ylabel('Net Energy / Linear Mass \n[MWh/tonne]')


single_deploy_2022 = pd.DataFrame(index=idx_temp, columns=['MW'], dtype=float)
single_deploy_2022['MW'] = 0.0
single_deploy_2022.loc[2022] = 100.0 #install a single 100 MW system


sim2 = PV_ICE.Simulation(name='sim2', path=testfolder)
for scen in scennames:
    sim2.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim2.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#use the settings from the first simulation to create the identical settings for sim2
for scen in scennames:
    sim2.scenario[scen].dataIn_m = sim1.scenario[scen].dataIn_m
    sim2.scenario[scen].dataIn_e = sim1.scenario[scen].dataIn_e
    for mat in MATERIALS:
        sim2.scenario[scen].material[mat].matdataIn_m = sim1.scenario[scen].material[mat].matdataIn_m
        sim2.scenario[scen].material[mat].matdataIn_e = sim1.scenario[scen].material[mat].matdataIn_e


sim2.scenario['Perovskite'].material['glass'].matdataIn_m


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim2.trim_Years(startYear=2000, endYear=2100)


#deployment projection for all scenarios
sim2.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', value= single_deploy_2022['MW'], start_year=2000)


sim2.scenario['Perovskite'].dataIn_m.loc[19:25]


for scen in scennames:
    sim2.calculateFlows(scenarios=[scen],bifacialityfactors=bifiPathDict[scen])


sim2_yearly, sim2_cumulative = sim2.aggregateResults()


allenergy2, energyGen2, energy_demands2 = sim2.aggregateEnergyResults()


allenergy2_cumu = allenergy2.cumsum()
energyGen2_cumu = energyGen2.cumsum()
energy_demands2_cumu = energy_demands2.cumsum()


energy_demands2_cumu_total = energy_demands2_cumu.filter(like='demand_total')
#energy_demands2_cumu_total


plt.bar(energy_demands2_cumu_total.columns, energy_demands2_cumu_total.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Energy Demands Cumulative')
plt.ylabel('Energy Demands [Wh]')


energyGen2_cumu.columns=energy_demands2_cumu_total.columns=scennames_labels
eroi_2022 = energyGen2_cumu.iloc[100]/energy_demands2_cumu_total.iloc[100]
eroi_2022


plt.bar(eroi_2022.index, eroi_2022, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI')
plt.ylabel('Arb.')


plt.plot(energyGen2/1e9)
plt.legend(scennames_labels)
plt.title('Energy Generation - Single 2022 System')
plt.ylabel('[GWh]')


single_deploy_2050 = pd.DataFrame(index=idx_temp, columns=['MW'], dtype=float)
single_deploy_2050['MW'] = 0.0
single_deploy_2050.loc[2050] = 100.0 #install a single 100 MW system


sim3 = PV_ICE.Simulation(name='sim3', path=testfolder)
for scen in scennames:
    sim3.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim3.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#use the settings from the first simulation to create the identical settings for sim2
for scen in scennames:
    sim3.scenario[scen].dataIn_m = sim1.scenario[scen].dataIn_m
    sim3.scenario[scen].dataIn_e = sim1.scenario[scen].dataIn_e
    for mat in MATERIALS:
        sim3.scenario[scen].material[mat].matdataIn_m = sim1.scenario[scen].material[mat].matdataIn_m
        sim3.scenario[scen].material[mat].matdataIn_e = sim1.scenario[scen].material[mat].matdataIn_e


sim3.scenario['Perovskite'].material['glass'].matdataIn_m


#deployment projection for all scenarios
sim3.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', value= single_deploy_2050['MW'], start_year=2000)


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim3.trim_Years(startYear=2000, endYear=2150)


sim3.scenario['Perovskite'].dataIn_e.tail(4)#loc[49:55]


#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
#need to adjust for length to 2150
idx_temp_2150 = pd.RangeIndex(start=2000,stop=2151,step=1) #create the index
df_temp_2150 = pd.DataFrame(index=idx_temp_2150, columns=['bifi'], dtype=float)
bifiPathDict_2150={}

for f in bifiFactors.keys(): #loop over module types
    bifi_2150 = df_temp_2150.copy() #copy of df
    bifi_2150['bifi'] = bifiFactors[f] #assign column
    bifipath_2150 = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi_2150.to_csv(path_or_buf=bifipath_2150, index_label='year') #create file
    bifiPathDict_2150[str(f)] = bifipath_2150
    #append bifi path to dict? or list?


for scen in scennames:
    sim3.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict_2150[scen])


sim3_yearly, sim3_cumulative = sim3.aggregateResults()


allenergy3, energyGen3, energy_demands3 = sim3.aggregateEnergyResults()


allenergy3_cumu = allenergy3.cumsum()
energyGen3_cumu = energyGen3.cumsum()
energy_demands3_cumu = energy_demands3.cumsum()


energy_demands3_cumu_total = energy_demands3_cumu.filter(like='demand_total')
#energy_demands3_cumu_total


plt.bar(energy_demands3_cumu_total.columns, energy_demands3_cumu_total.loc[2150], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Energy Demands Cumulative 2050 System')
plt.ylabel('Energy Demands [Wh]')


energyGen3_cumu.columns = energy_demands3_cumu_total.columns = scennames_labels
eroi_2050 = energyGen3_cumu.loc[2150]/energy_demands3_cumu_total.loc[2150]
eroi_2050


plt.bar(eroi_2050.index, eroi_2050, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI of a 100 MW deployment in 2050')
plt.ylabel('Arbitrary units')


plt.plot(energyGen3/1e9)
plt.legend(scennames_labels)
plt.title('Energy Generation - Single 2050 System')
plt.ylabel('[GWh]')

	E_MFG_kWhpkg	E_mfgFuelFraction
1995	600.817735	0.064489
1996	527.042434	0.072326
1997	485.167804	0.077357
1998	432.693843	0.085473
1999	380.620553	0.095834
2000	269.697933	0.075408
2001	236.759317	0.066235
2002	204.221370	0.054188
2003	172.084094	0.037719
2004	159.669155	0.030097
2005	140.254886	0.022532
2006	130.586287	0.022463
2007	121.318358	0.022639
2008	112.451100	0.023120
2009	103.984511	0.023976
2010	95.918593	0.025298
2011	88.253345	0.027195
2012	88.388767	0.027307
2013	88.924859	0.027745
2014	89.861622	0.028498
2015	91.199054	0.029547
2016	76.337157	0.037576
2017	61.875930	0.049814
2018	62.915373	0.053028
2019	64.355487	0.056410
2020	74.096270	0.053503
2021	77.837724	0.055738
2022	77.837724	0.055738
2023	77.837724	0.055738
2024	77.837724	0.055738
2025	77.837724	0.055738
2026	77.837724	0.055738
2027	77.837724	0.055738
2028	77.837724	0.055738
2029	77.837724	0.055738
2030	77.837724	0.055738
2031	77.837724	0.055738
2032	77.837724	0.055738
2033	77.837724	0.055738
2034	77.837724	0.055738
2035	77.837724	0.055738
2036	77.837724	0.055738
2037	77.837724	0.055738
2038	77.837724	0.055738
2039	77.837724	0.055738
2040	77.837724	0.055738
2041	77.837724	0.055738
2042	77.837724	0.055738
2043	77.837724	0.055738
2044	77.837724	0.055738
2045	77.837724	0.055738
2046	77.837724	0.055738
2047	77.837724	0.055738
2048	77.837724	0.055738
2049	77.837724	0.055738
2050	77.837724	0.055738

	year	e_mat_extraction	e_mat_MFG	e_mat_MFG_fuelfraction	e_mat_MFGScrap_LQ	e_mat_MFGScrap_HQ	e_mat_Landfill	e_mat_EoL_ReMFG_clean	e_mat_Recycled_LQ	e_mat_Recycled_HQ	e_mat_Recycled_HQ_fuelfraction
25	2020	0.0062	169.06	0.22	24.5	600.0	0.09	0.4	1.6	138.380000	0.200000
26	2021	0.0062	170.73	0.21	24.5	600.0	0.09	0.4	1.6	143.040000	0.190000
27	2022	0.0062	169.05	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738
28	2023	0.0062	168.80	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738
29	2024	0.0062	169.09	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738

	Area	Area_for_EOL_pathsG	Cumulative_Active_Area	EOL_BS_Year100	...	Status_BAD_[W]	WeibullParams	Yearly_Sum_Area_disposed	Yearly_Sum_Area_disposedby_Failure	Yearly_Sum_Area_disposedby_ProjectLifetime	Yearly_Sum_Power_disposed	Yearly_Sum_Power_disposedby_Failure	Yearly_Sum_Power_disposedby_ProjectLifetime	irradiance_stc
45	2.161127e+10	0.000000e+00	2.647719e+11	1.113640e+10	...	0.000000e+00	{'alpha': 5.692, 'beta': 29.697}	9.565400e+08	9.565400e+08	0.000000e+00	3.230205e+11	1.603521e+11	1.626684e+11	1000.0
46	2.282392e+10	1.513471e+08	2.861661e+11	1.176128e+10	...	0.000000e+00	{'alpha': 5.692, 'beta': 29.697}	1.429672e+09	1.200358e+09	2.293138e+08	3.960973e+11	2.008011e+11	1.952962e+11	1000.0
47	2.426058e+10	0.000000e+00	3.082348e+11	1.250160e+10	...	1.124231e+11	{'alpha': 5.692, 'beta': 29.697}	2.191898e+09	1.450143e+09	7.417553e+08	4.384001e+11	2.431038e+11	1.952962e+11	1000.0
48	2.573972e+10	0.000000e+00	3.309553e+11	1.326381e+10	...	3.108352e+11	{'alpha': 5.692, 'beta': 29.697}	3.019189e+09	1.722880e+09	1.296308e+09	4.844772e+11	2.891810e+11	1.952962e+11	1000.0
49	2.735478e+10	0.000000e+00	3.542727e+11	1.409606e+10	...	6.253774e+11	{'alpha': 5.692, 'beta': 29.697}	4.037412e+09	1.993949e+09	2.043463e+09	5.309042e+11	3.356080e+11	1.952962e+11	1000.0
50	2.875429e+10	0.000000e+00	3.783066e+11	1.481724e+10	...	1.014552e+12	{'alpha': 5.692, 'beta': 29.697}	4.720404e+09	2.226503e+09	2.493901e+09	5.722748e+11	3.769786e+11	1.952962e+11	1000.0
51	8.277558e+09	0.000000e+00	3.808927e+11	4.265469e+09	...	1.518172e+12	{'alpha': 5.692, 'beta': 29.697}	5.691420e+09	2.489763e+09	3.201657e+09	6.185813e+11	4.232851e+11	1.952962e+11	1000.0
52	8.834297e+09	0.000000e+00	3.833459e+11	4.552360e+09	...	2.100576e+12	{'alpha': 5.692, 'beta': 29.697}	6.381167e+09	2.719215e+09	3.661952e+09	6.602462e+11	4.649500e+11	1.952962e+11	1000.0
53	9.508524e+09	0.000000e+00	3.856261e+11	4.899792e+09	...	2.782309e+12	{'alpha': 5.692, 'beta': 29.697}	7.228292e+09	2.981736e+09	4.246556e+09	7.072519e+11	5.119557e+11	1.952962e+11	1000.0
54	1.019022e+10	0.000000e+00	3.877432e+11	5.251072e+09	...	3.564836e+12	{'alpha': 5.692, 'beta': 29.697}	8.073099e+09	3.243682e+09	4.829417e+09	7.544364e+11	5.591402e+11	1.952962e+11	1000.0
55	1.090978e+10	0.000000e+00	3.896927e+11	5.621866e+09	...	4.456191e+12	{'alpha': 5.692, 'beta': 29.697}	8.960321e+09	3.506488e+09	5.453833e+09	8.019221e+11	6.066259e+11	1.952962e+11	1000.0
56	1.157216e+10	0.000000e+00	3.914786e+11	5.963193e+09	...	5.446049e+12	{'alpha': 5.692, 'beta': 29.697}	9.786214e+09	3.754023e+09	6.032191e+09	8.471425e+11	6.518463e+11	1.952962e+11	1000.0
57	1.225090e+10	0.000000e+00	3.931001e+11	6.312955e+09	...	6.536422e+12	{'alpha': 5.692, 'beta': 29.697}	1.062945e+10	4.010127e+09	6.619324e+09	8.939589e+11	6.986627e+11	1.952962e+11	1000.0
58	1.292830e+10	0.000000e+00	3.945617e+11	6.662019e+09	...	7.723045e+12	{'alpha': 5.692, 'beta': 29.697}	1.146669e+10	4.289124e+09	7.177564e+09	9.447610e+11	7.494648e+11	1.952962e+11	1000.0
59	1.370120e+10	0.000000e+00	3.958380e+11	7.060299e+09	...	9.026005e+12	{'alpha': 5.692, 'beta': 29.697}	1.242490e+10	4.572062e+09	7.852835e+09	9.964887e+11	8.011925e+11	1.952962e+11	1000.0
60	1.441552e+10	0.000000e+00	3.969540e+11	7.428393e+09	...	1.043340e+13	{'alpha': 5.692, 'beta': 29.697}	1.329944e+10	4.847691e+09	8.451750e+09	1.047236e+12	8.519394e+11	1.952962e+11	1000.0
61	1.508835e+10	0.000000e+00	3.979283e+11	7.775106e+09	...	1.193640e+13	{'alpha': 5.692, 'beta': 29.697}	1.411413e+10	5.120519e+09	8.993611e+09	1.097673e+12	9.023766e+11	1.952962e+11	1000.0
62	1.587704e+10	0.000000e+00	3.987287e+11	8.181522e+09	...	1.356047e+13	{'alpha': 5.692, 'beta': 29.697}	1.507657e+10	5.393193e+09	9.683376e+09	1.148144e+12	9.528482e+11	1.952962e+11	1000.0
63	1.654507e+10	0.000000e+00	3.993973e+11	8.525760e+09	...	1.528184e+13	{'alpha': 5.692, 'beta': 29.697}	1.587653e+10	5.649434e+09	1.022710e+10	1.195826e+12	1.000530e+12	1.952962e+11	1000.0
64	1.727432e+10	0.000000e+00	3.999183e+11	8.901549e+09	...	1.711564e+13	{'alpha': 5.692, 'beta': 29.697}	1.675335e+10	5.896936e+09	1.085641e+10	1.241871e+12	1.046575e+12	1.952962e+11	1000.0
65	1.804612e+10	0.000000e+00	4.002818e+11	9.299262e+09	...	1.907521e+13	{'alpha': 5.692, 'beta': 29.697}	1.768258e+10	6.122478e+09	1.156010e+10	1.283914e+12	1.088618e+12	1.952962e+11	1000.0
66	1.869317e+10	0.000000e+00	4.005271e+11	9.632686e+09	...	2.113971e+13	{'alpha': 5.692, 'beta': 29.697}	1.844783e+10	6.311580e+09	1.213625e+10	1.319365e+12	1.124069e+12	1.952962e+11	1000.0
67	1.937908e+10	0.000000e+00	4.006440e+11	9.986144e+09	...	2.332420e+13	{'alpha': 5.692, 'beta': 29.697}	1.926224e+10	6.465679e+09	1.279657e+10	1.348310e+12	1.153014e+12	1.952962e+11	1000.0
68	2.008369e+10	0.000000e+00	4.006280e+11	1.034923e+10	...	2.564250e+13	{'alpha': 5.692, 'beta': 29.697}	2.009967e+10	6.566635e+09	1.353304e+10	1.367530e+12	1.172234e+12	1.952962e+11	1000.0
69	2.071387e+10	0.000000e+00	4.005012e+11	1.067396e+10	...	2.809144e+13	{'alpha': 5.692, 'beta': 29.697}	2.084065e+10	6.594731e+09	1.424592e+10	1.373552e+12	1.178256e+12	1.952962e+11	1000.0
70	2.119130e+10	0.000000e+00	4.003067e+11	1.091999e+10	...	3.065286e+13	{'alpha': 5.692, 'beta': 29.697}	2.138586e+10	6.537323e+09	1.484853e+10	1.364137e+12	1.168840e+12	1.952962e+11	1000.0
71	2.177890e+10	0.000000e+00	4.000148e+11	1.122278e+10	...	3.336739e+13	{'alpha': 5.692, 'beta': 29.697}	2.207072e+10	6.389012e+09	1.568171e+10	1.338327e+12	1.143031e+12	1.952962e+11	1000.0
72	2.239079e+10	0.000000e+00	3.996221e+11	1.153809e+10	...	3.626276e+13	{'alpha': 5.692, 'beta': 29.697}	2.278351e+10	6.114705e+09	1.666880e+10	1.290129e+12	1.094833e+12	1.952962e+11	1000.0
73	2.290722e+10	0.000000e+00	3.991588e+11	1.180421e+10	...	3.934524e+13	{'alpha': 5.692, 'beta': 29.697}	2.337048e+10	5.685399e+09	1.768508e+10	1.214661e+12	1.019364e+12	1.952962e+11	1000.0
74	2.336760e+10	0.000000e+00	3.986468e+11	1.204145e+10	...	4.263237e+13	{'alpha': 5.692, 'beta': 29.697}	2.387963e+10	5.084881e+09	1.879475e+10	1.109315e+12	9.140187e+11	1.952962e+11	1000.0
75	2.356761e+10	0.000000e+00	3.981623e+11	1.312730e+10	...	4.609950e+13	{'alpha': 5.692, 'beta': 29.697}	2.405212e+10	4.295803e+09	1.975632e+10	9.713476e+11	7.760514e+11	1.952962e+11	1000.0
76	1.158800e+10	0.000000e+00	4.007343e+11	4.266223e+08	...	4.709759e+13	{'alpha': 5.692, 'beta': 29.697}	9.016019e+09	3.328727e+09	5.687292e+09	8.029005e+11	6.076043e+11	1.952962e+11	1000.0
77	1.207486e+10	0.000000e+00	4.032014e+11	3.842079e+08	...	4.816281e+13	{'alpha': 5.692, 'beta': 29.697}	9.607776e+09	3.537963e+09	6.069813e+09	8.409048e+11	6.456086e+11	1.952962e+11	1000.0
78	1.263233e+10	0.000000e+00	4.055457e+11	3.404575e+08	...	4.930933e+13	{'alpha': 5.692, 'beta': 29.697}	1.028802e+10	3.754968e+09	6.533056e+09	8.802767e+11	6.849805e+11	1.952962e+11	1000.0
79	1.319500e+10	0.000000e+00	4.077659e+11	2.954451e+08	...	5.053804e+13	{'alpha': 5.692, 'beta': 29.697}	1.097480e+10	3.973369e+09	7.001429e+09	9.198943e+11	7.245981e+11	1.952962e+11	1000.0
80	1.377923e+10	0.000000e+00	4.098565e+11	2.515220e+08	...	5.185352e+13	{'alpha': 5.692, 'beta': 29.697}	1.168862e+10	4.192795e+09	7.495822e+09	9.596871e+11	7.643909e+11	1.952962e+11	1000.0
81	1.433096e+10	0.000000e+00	4.118257e+11	2.092963e+08	...	5.324887e+13	{'alpha': 5.692, 'beta': 29.697}	1.236176e+10	4.410833e+09	7.950924e+09	9.992258e+11	8.039296e+11	1.952962e+11	1000.0
82	1.489287e+10	0.000000e+00	4.136708e+11	1.707456e+08	...	5.472605e+13	{'alpha': 5.692, 'beta': 29.697}	1.304780e+10	4.630530e+09	8.417273e+09	1.039029e+12	8.437331e+11	1.952962e+11	1000.0
83	1.545399e+10	0.000000e+00	4.153916e+11	1.363876e+08	...	5.628492e+13	{'alpha': 5.692, 'beta': 29.697}	1.373315e+10	4.850453e+09	8.882693e+09	1.078825e+12	8.835288e+11	1.952962e+11	1000.0
84	1.606664e+10	0.000000e+00	4.169747e+11	1.069144e+08	...	5.793698e+13	{'alpha': 5.692, 'beta': 29.697}	1.448350e+10	5.069768e+09	9.413733e+09	1.118439e+12	9.231425e+11	1.952962e+11	1000.0
85	1.664041e+10	0.000000e+00	4.184295e+11	8.165511e+07	...	5.967517e+13	{'alpha': 5.692, 'beta': 29.697}	1.518570e+10	5.281177e+09	9.904524e+09	1.156564e+12	9.612678e+11	1.952962e+11	1000.0
86	1.718538e+10	0.000000e+00	4.197623e+11	6.068386e+07	...	6.149449e+13	{'alpha': 5.692, 'beta': 29.697}	1.585257e+10	5.485759e+09	1.036681e+10	1.193355e+12	9.980584e+11	1.952962e+11	1000.0
87	1.778734e+10	0.000000e+00	4.209577e+11	4.397857e+07	...	6.340891e+13	{'alpha': 5.692, 'beta': 29.697}	1.659190e+10	5.683207e+09	1.090870e+10	1.228713e+12	1.033417e+12	1.952962e+11	1000.0
88	1.830806e+10	0.000000e+00	4.220351e+11	3.072108e+07	...	6.540388e+13	{'alpha': 5.692, 'beta': 29.697}	1.723063e+10	5.862945e+09	1.136768e+10	1.260748e+12	1.065452e+12	1.952962e+11	1000.0
89	1.884899e+10	0.000000e+00	4.229880e+11	2.070074e+07	...	6.748678e+13	{'alpha': 5.692, 'beta': 29.697}	1.789617e+10	6.027435e+09	1.186873e+10	1.289848e+12	1.094551e+12	1.952962e+11	1000.0
90	1.939256e+10	0.000000e+00	4.238139e+11	1.335170e+07	...	6.966274e+13	{'alpha': 5.692, 'beta': 29.697}	1.856668e+10	6.167667e+09	1.239902e+10	1.314397e+12	1.119101e+12	1.952962e+11	1000.0
91	1.983963e+10	0.000000e+00	4.245355e+11	8.127679e+06	...	7.191672e+13	{'alpha': 5.692, 'beta': 29.697}	1.911800e+10	6.274416e+09	1.284358e+10	1.332759e+12	1.137463e+12	1.952962e+11	1000.0
92	2.027973e+10	0.000000e+00	4.251528e+11	4.632936e+06	...	7.425341e+13	{'alpha': 5.692, 'beta': 29.697}	1.966242e+10	6.347567e+09	1.331486e+10	1.344901e+12	1.149605e+12	1.952962e+11	1000.0
93	2.069356e+10	0.000000e+00	4.256707e+11	2.426359e+06	...	7.667505e+13	{'alpha': 5.692, 'beta': 29.697}	2.017569e+10	6.376714e+09	1.379897e+10	1.349010e+12	1.153714e+12	1.952962e+11	1000.0
94	2.102219e+10	0.000000e+00	4.261092e+11	1.133269e+06	...	7.917268e+13	{'alpha': 5.692, 'beta': 29.697}	2.058365e+10	6.351695e+09	1.423195e+10	1.343349e+12	1.148053e+12	1.952962e+11	1000.0
95	2.121904e+10	0.000000e+00	4.265008e+11	4.513219e+05	...	8.172788e+13	{'alpha': 5.692, 'beta': 29.697}	2.082749e+10	6.267508e+09	1.455998e+10	1.327114e+12	1.131818e+12	1.952962e+11	1000.0
96	2.142881e+10	0.000000e+00	4.268415e+11	1.433457e+05	...	8.435394e+13	{'alpha': 5.692, 'beta': 29.697}	2.108804e+10	6.124329e+09	1.496371e+10	1.300438e+12	1.105142e+12	1.952962e+11	1000.0
97	2.159338e+10	0.000000e+00	4.271434e+11	3.140321e+04	...	8.705377e+13	{'alpha': 5.692, 'beta': 29.697}	2.129153e+10	5.907410e+09	1.538412e+10	1.260889e+12	1.065593e+12	1.952962e+11	1000.0
98	2.164088e+10	0.000000e+00	4.274375e+11	3.409068e+03	...	8.981587e+13	{'alpha': 5.692, 'beta': 29.697}	2.134674e+10	5.607793e+09	1.573894e+10	1.207095e+12	1.011799e+12	1.952962e+11	1000.0
99	2.159428e+10	0.000000e+00	4.277507e+11	6.709960e+01	...	9.263348e+13	{'alpha': 5.692, 'beta': 29.697}	2.128112e+10	5.225862e+09	1.605526e+10	1.139357e+12	9.440608e+11	1.952962e+11	1000.0
100	2.134402e+10	0.000000e+00	4.281388e+11	0.000000e+00	...	9.547521e+13	{'alpha': 5.692, 'beta': 29.697}	2.095592e+10	4.763241e+09	1.619268e+10	1.058210e+12	8.629142e+11	1.952962e+11	1000.0

Energy Analysis¶

Module Types Creation¶

Extreme modules¶

PERC_50¶

High Efficiency¶

Idealized Perovskite¶

Lightweighting¶

Hypothetical Modules¶

50-year PERC gradual improvement¶

Recycled PERC¶

Hopeful Perovskite, Gradually increasing circularity¶

Commercialized Modules with Expected Continued Improvement¶

PERC¶

SHJ¶

TOPCon¶

IRENA Reg. Loss with improved recycling¶

Modify Years 2000 to 2100¶

Apply deployment curve¶

Calculate Mass flow¶

Installation Compensation¶

RESULTS: Effective Capacity and Replacements¶

RESULTS: Virgin Material Demands¶

RESULTS: Lifecycle Wastes¶

RESULTS: Energy¶

Energy Generation¶

Net Energy¶

System Level EROI¶

CE-TE Metric¶

EROI¶

EROI 2022¶

EROI of 2050 module¶

To Do¶